The selection of the Reno-based drone operator Flirtey and its local partners for a national test program aimed at increasing the use of unmanned aircraft will be a "game-changer" for the delivery of emergency medical supplies in the region, backers of the effort say. The 10 sites the U.S. Department of Transportation announced Wednesday include projects ranging from monitoring crops and oil pipelines in North Dakota to applying mosquito-killing treatments in Florida. In northern Nevada, the focus will be on drugs and medical equipment. Flirtey drones already have delivered automated external defibrillators used to jumpstart the hearts of cardiac arrest victims as part of a joint emergency program with first-responders in Reno. The company also anticipates future deliveries of EpiPens to treat severe allergic reactions and Narcan for opioid overdoses.

Now that computer scientists at Lawrence Berkeley National Laboratory's National Energy Research Scientific Computing Center (NERSC) have demonstrated 15 petaflops deep-learning training performance on the Cray Cori supercomputer, the NERSC staff is working to address the data management issues that arise when running production deep-learning codes at such scale. The existing deep learning tools were not designed to efficiently ingest or manage the terabyte- to petabyte-sized deep-learning training sets that scientists can now use on this leadership class supercomputer. "Enabling the NERSC user community to perform deep learning at scale on Cori," Quincey Koziol (Staff, Berkeley Lab) observes, "means scientists can use deep learning as part of their leading-edge scientific efforts." Thus NERSC staff are working to break new ground in adapting existing deep-learning frameworks to run efficiently at scale on thousands of nodes while giving researchers the ability to create and manage training sets containing tens to hundreds of terabytes of data in a portable fashion. For these datasets, it is imperative that they are formatted so Cori can ingest them efficiently at runtime.

There are so many amazing ways artificial intelligence and machine learning are used behind the scenes to impact our everyday lives and inform business decisions and optimize operations for some of the world's leading companies. Here are 27 amazing practical examples of AI and machine learning. Using natural language processing, machine learning and advanced analytics, Hello Barbie listens and responds to a child. A microphone on Barbie's necklace records what is said and transmits it to the servers at ToyTalk. There, the recording is analyzed to determine the appropriate response from 8,000 lines of dialogue.

Makers of giant wind turbines are hoping that artificial intelligence can bring back some of the industry's mojo. While developers have spent $1.1 trillion on new wind farms over the past dozen years--helping transform the global energy landscape with renewable power--more money is going into new solar systems these days. Also, governments are phasing out subsidies, including programs in the U.S. that have offered $22 billion in tax breaks to turbine projects in the past 15 years. To remain an attractive, lower-cost option for utilities, companies like Vestas Wind Systems A/S and Invenergy LLC are investing in technologies to squeeze more electricity from every propeller rotation. Modern turbines with blades that stretch 450 feet (137 meters) in the air already can twist and turn and spin faster or slower to adjust to ever-changing breezes.

We study an interacting particle system in $\mathbf{R}^d$ motivated by Stein variational gradient descent [Q. Liu and D. Wang, NIPS 2016], a deterministic algorithm for sampling from a given probability density with unknown normalization. We prove that in the large particle limit the empirical measure converges to a solution of a non-local and nonlinear PDE. We also prove global well-posedness and uniqueness of the solution to the limiting PDE. Finally, we prove that the solution to the PDE converges to the unique invariant solution in large time limit.

Abstract--Solar forecasting accuracy is affected by weather conditions, and weather awareness forecasting models are expected to improve the performance. However, it may not be available and reliable to classify different forecasting tasks by using only meteorological weather categorization. In this paper, an unsupervised clustering-based (UC-based) solar forecasting methodology is developed for short-term (1-hour-ahead) global horizontal irradiance (GHI) forecasting. This methodology consists of three parts: GHI time series unsupervised clustering, pattern recognition, and UC-based forecasting. The daily GHI time series is first clustered by an Optimized Cross-validated ClUsteRing (OCCUR) method, which determines the optimal number of clusters and best clustering results. Then, support vector machine pattern recognition (SVM-PR) is adopted to recognize the category of a certain day using the first few hours data in the forecasting stage. GHI forecasts are generated by the most suitable models in different clusters, which are built by a two-layer Machine learning based Multi-Model (M3) forecasting framework. The developed UC-based methodology is validated by using 1-year of data with six solar features. Numerical results show that (i) UC-based models outperform non-UC (all-in-one) models with the same M3 architecture by approximately 20%; (ii) M3-based models also outperform the single-algorithm machine learning (SAML) models by approximately 20%.

If the invention of the ship was also the invention of the shipwreck, as the French philosopher Paul Virilio suggested, then what does that make the invention of the Nest learning thermostat? As our homes fill up with more connected devices, funnelling every aspect of our lives into the great cloud of big data, the answer could be something much more alarming than just a few more faulty appliances cluttering up our cupboards. This is one of the unsettling questions at the heart of The Future Starts Here, an exhibition about to open at the V&A in London. It promises to be less of a showcase of Tomorrow's World-type gadgetry than a thought-provoking probe into where exactly this new generation of smart technology is taking us. "People seem scared of the future at the moment," says Rory Hyde who, with co-curator Mariana Pestana, has spent the last two years trawling university laboratories and touring Silicon Valley to gather 100 hot-out-of-the-factory innovations, from a low-cost satellite to a solar-powered shirt that can charge a smartphone.

Classical methods to control heating systems are often marred by suboptimal performance, inability to adapt to dynamic conditions and unreasonable assumptions e.g. existence of building models. This paper presents a novel deep reinforcement learning algorithm which can control space heating in buildings in a computationally efficient manner, and benchmarks it against other known techniques. The proposed algorithm outperforms rule based control by between 5-10% in a simulation environment for a number of price signals. We conclude that, while not optimal, the proposed algorithm offers additional practical advantages such as faster computation times and increased robustness to non-stationarities in building dynamics.

DALLAS (AP) -- US oil has shot above $70 a barrel for the first time since late 2014, foreshadowing costlier gasoline and consumer goods. It's not clear that higher crude prices will threaten economic growth, however, and stocks are moving higher. Many factors are behind the increase including the possibility that President Donald Trump will scrap the deal that eased sanctions on Iran. That could pinch exports from that key oil producer. SEATTLE (AP) -- Nestle is paying more than $7 billion to handle global retail sales of Starbucks's coffee and tea outside of its coffee shops.

Denmark generated 74% of its energy from renewable sources in 2017, making it a world leader in renewable power. But it's not the country's major energy providers that are driving this move away from fossil fuels. Rather, it is an increase in so-called "energy citizens" -- people that are decentralising the energy mix and installing smaller, renewable power projects. This is encouraged by government policies such as granting permits to wind projects only if the developers are at least 20 percent owned by local communities. It's not just Denmark -- across much of Europe energy citizens are playing an ever-more important role.